{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here I will to show how to use linear model stochastic gradient descent on multi-class classification/discrimination\n",
    "\n",
    "import class sklearn.linear_model.SGDClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/dist-packages/sklearn/cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "import numpy as np\n",
    "import sklearn.datasets\n",
    "import re\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.cross_validation import train_test_split\n",
    "import xgboost as xgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.4'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define some functions to help us on preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# clear string\n",
    "def clearstring(string):\n",
    "    string = re.sub('[^A-Za-z ]+', '', string)\n",
    "    string = string.split(' ')\n",
    "    string = filter(None, string)\n",
    "    string = [y.strip() for y in string]\n",
    "    string = ' '.join(string)\n",
    "    return string.lower()\n",
    "\n",
    "# because of sklean.datasets read a document as a single element\n",
    "# so we want to split based on new line\n",
    "def separate_dataset(trainset):\n",
    "    datastring = []\n",
    "    datatarget = []\n",
    "    for i in range(len(trainset.data)):\n",
    "        data_ = trainset.data[i].split('\\n')\n",
    "        # python3, if python2, just remove list()\n",
    "        data_ = list(filter(None, data_))\n",
    "        for n in range(len(data_)):\n",
    "            data_[n] = clearstring(data_[n])\n",
    "        datastring += data_\n",
    "        for n in range(len(data_)):\n",
    "            datatarget.append(trainset.target[i])\n",
    "    return datastring, datatarget"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['anger', 'fear', 'joy', 'love', 'sadness', 'surprise']\n",
      "416809\n",
      "416809\n"
     ]
    }
   ],
   "source": [
    "# you can change any encoding type\n",
    "trainset = sklearn.datasets.load_files(container_path = 'data', encoding = 'UTF-8')\n",
    "trainset.data, trainset.target = separate_dataset(trainset)\n",
    "print (trainset.target_names)\n",
    "print (len(trainset.data))\n",
    "print (len(trainset.target))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "bow = CountVectorizer(min_df=10).fit(trainset.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "out = bow.transform(trainset.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "trainset.target = np.array(trainset.target)\n",
    "train_X, test_X, train_Y, test_Y = train_test_split(out, trainset.target, test_size = 0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Will train until validation_1 error hasn't decreased in 20 rounds.\n",
      "[0]\tvalidation_0-mlogloss:1.748559\tvalidation_1-mlogloss:1.748552\n",
      "[1]\tvalidation_0-mlogloss:1.708754\tvalidation_1-mlogloss:1.708001\n",
      "[2]\tvalidation_0-mlogloss:1.669859\tvalidation_1-mlogloss:1.669619\n",
      "[3]\tvalidation_0-mlogloss:1.634101\tvalidation_1-mlogloss:1.634130\n",
      "[4]\tvalidation_0-mlogloss:1.603129\tvalidation_1-mlogloss:1.602402\n",
      "[5]\tvalidation_0-mlogloss:1.570976\tvalidation_1-mlogloss:1.570432\n",
      "[6]\tvalidation_0-mlogloss:1.541222\tvalidation_1-mlogloss:1.541072\n",
      "[7]\tvalidation_0-mlogloss:1.513381\tvalidation_1-mlogloss:1.513232\n",
      "[8]\tvalidation_0-mlogloss:1.487926\tvalidation_1-mlogloss:1.487376\n",
      "[9]\tvalidation_0-mlogloss:1.461228\tvalidation_1-mlogloss:1.460954\n",
      "[10]\tvalidation_0-mlogloss:1.438190\tvalidation_1-mlogloss:1.437731\n",
      "[11]\tvalidation_0-mlogloss:1.415609\tvalidation_1-mlogloss:1.415365\n",
      "[12]\tvalidation_0-mlogloss:1.392168\tvalidation_1-mlogloss:1.391810\n",
      "[13]\tvalidation_0-mlogloss:1.372123\tvalidation_1-mlogloss:1.371460\n",
      "[14]\tvalidation_0-mlogloss:1.352773\tvalidation_1-mlogloss:1.351897\n",
      "[15]\tvalidation_0-mlogloss:1.333238\tvalidation_1-mlogloss:1.332498\n",
      "[16]\tvalidation_0-mlogloss:1.315125\tvalidation_1-mlogloss:1.314413\n",
      "[17]\tvalidation_0-mlogloss:1.299127\tvalidation_1-mlogloss:1.298033\n",
      "[18]\tvalidation_0-mlogloss:1.281368\tvalidation_1-mlogloss:1.280300\n",
      "[19]\tvalidation_0-mlogloss:1.265562\tvalidation_1-mlogloss:1.264497\n",
      "[20]\tvalidation_0-mlogloss:1.250038\tvalidation_1-mlogloss:1.248896\n",
      "[21]\tvalidation_0-mlogloss:1.235340\tvalidation_1-mlogloss:1.234360\n",
      "[22]\tvalidation_0-mlogloss:1.222387\tvalidation_1-mlogloss:1.221353\n",
      "[23]\tvalidation_0-mlogloss:1.208632\tvalidation_1-mlogloss:1.207541\n",
      "[24]\tvalidation_0-mlogloss:1.195736\tvalidation_1-mlogloss:1.194884\n",
      "[25]\tvalidation_0-mlogloss:1.183785\tvalidation_1-mlogloss:1.183131\n",
      "[26]\tvalidation_0-mlogloss:1.171702\tvalidation_1-mlogloss:1.170993\n",
      "[27]\tvalidation_0-mlogloss:1.160034\tvalidation_1-mlogloss:1.159110\n",
      "[28]\tvalidation_0-mlogloss:1.148212\tvalidation_1-mlogloss:1.147306\n",
      "[29]\tvalidation_0-mlogloss:1.136586\tvalidation_1-mlogloss:1.135423\n",
      "[30]\tvalidation_0-mlogloss:1.124582\tvalidation_1-mlogloss:1.123422\n",
      "[31]\tvalidation_0-mlogloss:1.113835\tvalidation_1-mlogloss:1.112782\n",
      "[32]\tvalidation_0-mlogloss:1.103618\tvalidation_1-mlogloss:1.102686\n",
      "[33]\tvalidation_0-mlogloss:1.093404\tvalidation_1-mlogloss:1.092569\n",
      "[34]\tvalidation_0-mlogloss:1.083350\tvalidation_1-mlogloss:1.082525\n",
      "[35]\tvalidation_0-mlogloss:1.073672\tvalidation_1-mlogloss:1.072945\n",
      "[36]\tvalidation_0-mlogloss:1.064583\tvalidation_1-mlogloss:1.063787\n",
      "[37]\tvalidation_0-mlogloss:1.056279\tvalidation_1-mlogloss:1.055469\n",
      "[38]\tvalidation_0-mlogloss:1.046628\tvalidation_1-mlogloss:1.045881\n",
      "[39]\tvalidation_0-mlogloss:1.038865\tvalidation_1-mlogloss:1.037960\n",
      "[40]\tvalidation_0-mlogloss:1.029814\tvalidation_1-mlogloss:1.028770\n",
      "[41]\tvalidation_0-mlogloss:1.021853\tvalidation_1-mlogloss:1.020755\n",
      "[42]\tvalidation_0-mlogloss:1.013378\tvalidation_1-mlogloss:1.012290\n",
      "[43]\tvalidation_0-mlogloss:1.005711\tvalidation_1-mlogloss:1.004851\n",
      "[44]\tvalidation_0-mlogloss:0.997773\tvalidation_1-mlogloss:0.997043\n",
      "[45]\tvalidation_0-mlogloss:0.989756\tvalidation_1-mlogloss:0.989038\n",
      "[46]\tvalidation_0-mlogloss:0.981850\tvalidation_1-mlogloss:0.981471\n",
      "[47]\tvalidation_0-mlogloss:0.974764\tvalidation_1-mlogloss:0.974382\n",
      "[48]\tvalidation_0-mlogloss:0.967764\tvalidation_1-mlogloss:0.967356\n",
      "[49]\tvalidation_0-mlogloss:0.960299\tvalidation_1-mlogloss:0.959687\n",
      "[50]\tvalidation_0-mlogloss:0.954011\tvalidation_1-mlogloss:0.953484\n",
      "[51]\tvalidation_0-mlogloss:0.947583\tvalidation_1-mlogloss:0.947119\n",
      "[52]\tvalidation_0-mlogloss:0.940905\tvalidation_1-mlogloss:0.940448\n",
      "[53]\tvalidation_0-mlogloss:0.934100\tvalidation_1-mlogloss:0.933752\n",
      "[54]\tvalidation_0-mlogloss:0.928035\tvalidation_1-mlogloss:0.927660\n",
      "[55]\tvalidation_0-mlogloss:0.922244\tvalidation_1-mlogloss:0.921843\n",
      "[56]\tvalidation_0-mlogloss:0.916246\tvalidation_1-mlogloss:0.915864\n",
      "[57]\tvalidation_0-mlogloss:0.910207\tvalidation_1-mlogloss:0.909818\n",
      "[58]\tvalidation_0-mlogloss:0.904546\tvalidation_1-mlogloss:0.904071\n",
      "[59]\tvalidation_0-mlogloss:0.898914\tvalidation_1-mlogloss:0.898586\n",
      "[60]\tvalidation_0-mlogloss:0.892891\tvalidation_1-mlogloss:0.892678\n",
      "[61]\tvalidation_0-mlogloss:0.887645\tvalidation_1-mlogloss:0.887469\n",
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      "[67]\tvalidation_0-mlogloss:0.854979\tvalidation_1-mlogloss:0.855116\n",
      "[68]\tvalidation_0-mlogloss:0.850166\tvalidation_1-mlogloss:0.850215\n",
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      "[75]\tvalidation_0-mlogloss:0.816008\tvalidation_1-mlogloss:0.816311\n",
      "[76]\tvalidation_0-mlogloss:0.811078\tvalidation_1-mlogloss:0.811487\n",
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      "[106]\tvalidation_0-mlogloss:0.696603\tvalidation_1-mlogloss:0.698476\n",
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      "[121]\tvalidation_0-mlogloss:0.652255\tvalidation_1-mlogloss:0.654838\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[122]\tvalidation_0-mlogloss:0.649581\tvalidation_1-mlogloss:0.652214\n",
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     ]
    },
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     "output_type": "stream",
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      "[1684]\tvalidation_0-mlogloss:0.165744\tvalidation_1-mlogloss:0.206226\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[1803]\tvalidation_0-mlogloss:0.162854\tvalidation_1-mlogloss:0.205920\n",
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      "[1816]\tvalidation_0-mlogloss:0.162562\tvalidation_1-mlogloss:0.205894\n",
      "Stopping. Best iteration:\n",
      "[1796]\tvalidation_0-mlogloss:0.163010\tvalidation_1-mlogloss:0.205882\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, colsample_bylevel=1, colsample_bytree=0.4,\n",
       "       gamma=0.65, learning_rate=0.1, max_delta_step=1.8, max_depth=7,\n",
       "       min_child_weight=10.0, missing=None, n_estimators=10000, nthread=-1,\n",
       "       objective='multi:softprob', reg_alpha=0, reg_lambda=1,\n",
       "       scale_pos_weight=1, seed=0, silent=False, subsample=0.8)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "params_xgd = {\n",
    "    'min_child_weight': 10.0,\n",
    "    'max_depth': 7,\n",
    "    'objective': 'multi:softprob',\n",
    "    'max_delta_step': 1.8,\n",
    "    'colsample_bytree': 0.4,\n",
    "    'subsample': 0.8,\n",
    "    'learning_rate': 0.1,\n",
    "    'gamma': 0.65,\n",
    "    'nthread': -1,\n",
    "    'silent': False,\n",
    "    'n_estimators': 10000\n",
    "    }\n",
    "clf = xgb.XGBClassifier(**params_xgd)\n",
    "clf.fit(train_X,train_Y, eval_set=[(train_X,train_Y), (test_X,test_Y)], \n",
    "        eval_metric='mlogloss', early_stopping_rounds=20, verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy validation set:  0.895132074566\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "      anger       0.88      0.92      0.90     11421\n",
      "       fear       0.83      0.84      0.84      9505\n",
      "        joy       0.93      0.91      0.92     28132\n",
      "       love       0.76      0.79      0.78      6801\n",
      "    sadness       0.95      0.94      0.94     24481\n",
      "   surprise       0.70      0.72      0.71      3022\n",
      "\n",
      "avg / total       0.90      0.90      0.90     83362\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "predicted = clf.predict(test_X)\n",
    "print('accuracy validation set: ', np.mean(predicted == test_Y))\n",
    "\n",
    "# print scores\n",
    "print(metrics.classification_report(test_Y, predicted, target_names = trainset.target_names))"
   ]
  }
 ],
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